System and method for estimating aircraft flight delay

ABSTRACT

A method and system estimate, in real time, the amount of any delay in departure of an aircraft flight from an airport. Various embodiments include receiving in a first computer process a conditions input that includes at least one member of a set including departure airport conditions, arrival airport conditions, and fleet conditions; and estimating in a second computer process the amount of delay based on the conditions input. User interfaces and data structures are also provided.

The present application is a continuation in part of application Ser. No. 09/636,367, filed Aug. 11, 2000, now U.S. Pat. No. 6,393,359; the latter application claims priority from provisional application Ser. No. 60/171,778, filed Dec. 22, 1999, and provisional application Ser. No. 60/195,776, filed Apr. 10, 2000. This application also claims priority from provisional application Ser. No. 60/257,497, filed Dec. 21, 2000, and provisional application Ser. No. 60/299,149, filed Jun. 18, 2001. All of the foregoing applications, which are for inventions by the present inventors, are hereby incorporated herein by reference.

TECHNICAL FIELD

The present invention relates to systems and methods for estimating delays from planned departure times of aircraft flights.

BACKGROUND ART

It is known in the prior art to utilize real-time flight data to estimate the landing time of aircraft that is in flight.

SUMMARY OF THE INVENTION

In a first embodiment of the invention there is provided a method of estimating, in real time, the amount of any delay, from a planned departure time, in departure of an aircraft flight from an airport. In this connection, for purposes of reference, it is considered that the aircraft belongs to a fleet (even if the fleet has only a single aircraft). Also for reference purposes, the flight is associated with a departure airport and an arrival airport. The method of this embodiment includes:

a. receiving in a first computer process a conditions input that includes at least one member of a set including departure airport conditions, arrival airport conditions, and fleet conditions; and

b. estimating in a second computer process the amount of delay based on the conditions input.

(For purposes of this description and the following claims, the first and second processes can be distinct processes, or the second process may be part of the first process.)

In further related embodiments, the conditions input includes at least two members of the set (optionally all three members of the set). Additionally, estimating the amount of delay includes separately determining a delay contribution from each member of the set included in the conditions input. Under circumstances wherein the conditions input includes departure airport conditions, determining the delay contribution from the departure airport conditions may include determining, for the departure airport, the departure demand and the departure capacity.

In a further related embodiment, the departure airport conditions may include weather at the departure airport, at an applicable departure time (or time interval), and determining departure capacity at the departure airport includes evaluating a departure capacity function mapping weather conditions to capacity based on weather. The departure capacity function may be a table that is updated in real time on the basis of live air traffic data and weather condition data. In addition, determining departure demand may include accessing a flight schedule database and, optionally, live air traffic data. Also optionally, the flight schedule database may be updated in real time on the basis of live air traffic data.

Determining the delay contribution from the departure airport conditions may include evaluating a first delay function of departure demand and departure capacity to obtain a preliminary departure delay contribution. Additionally, determining the delay contribution from the departure airport conditions may include determining a recent average departure delay and evaluating a second delay function of the recent average departure delay and the preliminary departure delay contribution.

Analogous embodiments permit consideration of arrival airport conditions. Hence the conditions input may include arrival airport conditions, and determining the delay contribution from the arrival airport conditions then includes determining, for the arrival airport, the arrival demand and the arrival capacity.

In a further related embodiment, the arrival airport conditions include weather at the arrival airport, at an applicable arrival time, and determining arrival capacity at the arrival airport includes evaluating an arrival capacity function mapping weather conditions to capacity based on weather. The arrival capacity function may be a table that is updated in real time on the basis of live air traffic data and weather condition data.

In addition, determining arrival demand may include accessing a flight schedule database and, optionally, live air traffic data. The flight schedule database may be updated in real time on the basis of live air traffic data.

In related embodiments, determining the delay contribution from the arrival airport conditions includes evaluating a first delay function of arrival demand and arrival capacity to obtain a preliminary arrival delay contribution. Determining the arrival delay contribution from the arrival airport conditions may include determining a recent average arrival delay and evaluating a second delay function of the recent average arrival delay and the preliminary arrival delay contribution.

In other related embodiments, determining the delay contribution from fleet conditions includes determining when an aircraft is first likely to be available for the planned flight. In turn, determining when an aircraft is first likely to be available may include (i) accessing a flight segment database identifying, for an aircraft of the planned flight, an immediately previous flight number and departing airport; and (ii) estimating a landing time when the immediately previous flight shall have landed at the departure airport. In a further embodiment, if the landing time estimated is later by more than a threshold amount than the planned departure time, then determining when an aircraft is first likely to be available includes determining when an alternative aircraft is first likely to be available.

In another embodiment, determining the delay contribution from fleet conditions includes accessing historical fleet performance data providing historical performance of the fleet. Optionally, the historical fleet performance data include on-time performance data.

In yet another embodiment, determining the delay contribution from at least one of departure airport conditions and arrival airport conditions includes using official airport delay data. Optionally, determining the delay contribution from each of departure airport conditions and arrival airport conditions includes using official airport delay data.

In another embodiment, the invention provides a system for estimating, in real time, the amount of any delay, from a planned departure time, in departure of an aircraft flight from an airport. In this embodiment, the aircraft belongs to a fleet, the flight is associated with a departure airport and an arrival airport. The system of this embodiment includes:

a first computer process for receiving a conditions input that includes at least one member of a set including departure airport conditions, arrival airport conditions, and fleet conditions; and

a second computer process for estimating the amount of delay based on the conditions input.

In a further related embodiment, (i) the conditions input includes at least two members of the set and (ii) the second computer process for estimating the amount of delay includes processes for separately determining a delay contribution from each member of the set included in the conditions input.

In another embodiment, the invention provides a digital electronic storage medium containing data correlating, with each of a series of at least three quantized weather conditions, the capacity of an airport to support departing flights. In a further related embodiment, the digital electronic storage medium contains data, correlating with each of a series of at least four quantized weather conditions, the capacity of an airport to support departing flights. Another embodiment provides a digital electronic storage medium containing data correlating, with each of a series of at least three quantized weather conditions, the capacity of an airport to support arriving flights. In a further embodiment, there is provided a digital electronic storage medium containing data, correlating with each of a series of at least four quantized weather conditions, the capacity of an airport to support arriving flights.

In yet another embodiment, there is provided a system for estimating, in real time, the amount of any delay, from a planned departure time, in departure of an aircraft flight from an airport. The system embodiment includes:

a user entry process permitting a user to generate a delay query that provides flight parameters over a communications network sufficient to determine the aircraft flight;

a delay determination process, in communication with the user entry process, that, substantially contemporaneously with the delay query, estimates a delay parameter associated with any delay in departure of the aircraft flight specified by the query; and

a presentation process, in communication with the delay determination process, that presents to the user the delay parameter.

Alternatively, or in addition, the delay determination process estimates a delay parameter associated with any delay in departure of the aircraft flight specified by the query, the delay parameter being a measure of the probability of a delay in departure. The measure may be discrete or continuous. If it is discrete it may be at least bi-valued and optionally at least tri-valued.

Alternatively or in addition, the delay parameter may be an estimate of at least one of the most probable time of departure and the most probable amount of delay in departure. Also alternatively, the presentation process presents to the user delay information corresponding to the delay parameter. The delay information may be a notification delivered a specified duration before the most probable time of departure. In a further embodiment, the notification is delivered over a network and triggers an alarm.

In another embodiment of the present invention there is provided a method of deriving, as a function of weather, the capacity of an airport to handle aircraft departures and aircraft arrivals. The method of this embodiment includes:

providing historical flight data for airport, over a plurality of specified time intervals, including actual departures and actual arrivals, weather conditions, and demand for departures and demand for arrivals;

selecting occasions in such intervals when demand for departures and demand for such arrivals exceeds actual arrivals and actual departures;

quantizing weather conditions for such occasions to produce quantized data identifying weather conditions for each such occasion; and

determining capacity of the airport to handle aircraft departures and aircraft arrivals from data pertinent to such occasions as a function of quantized weather values.

Optionally, capacity for departures and arrivals may instead be determined separately, in which case the above method is simplified, since the other of departures and arrivals may be ignored. Alternatively, capacity may be determined taking into account interaction between arrival and departure capacity. In a further embodiment therefore, determining capacity of the airport includes assuming, for each quantized weather value, a total capacity for departures and arrivals, such total capacity being the sum of arrival capacity and departure capacity. Optionally, assuming such total capacity further includes assuming a priority for providing arrival capacity sufficient to service arrival demand ahead of providing departure capacity sufficient to service departure demand.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing features of the invention will be more readily understood by reference to the following detailed description, taken with reference to the accompanying drawings, in which:

FIG. 1 is a block diagram providing an overview of data flow in accordance with a preferred embodiment of a method in accordance with the present invention;

FIG. 2 is a block diagram showing further detail of data flow for calculation of the departure airport conditions delay contribution in accordance with block 11 of FIG. 1;

FIG. 3 is a block diagram showing further detail of data flow for calculation of the arrival airport conditions delay contribution in accordance with block 12 of FIG. 1;

FIG. 4 is a block diagram showing yet further detail of the data flow of FIGS. 2 and 3 in connection with the calculation of departure airport conditions delay contribution and arrival airport conditions delay contribution;

FIG. 5 is a block diagram showing further detail of the data flow in connection with the fleet conditions delay contribution in accordance with block 13 of FIG. 1;

FIG. 6 is a block diagram of a system, in accordance with an embodiment of the present invention, for carrying out the methods of FIGS. 1 through 5; and

FIG. 7 is a block diagram of a system, in accordance with an embodiment of the present invention, providing a user interface for accessing departure delay information.

FIG. 8 is a block diagram of a method, in accordance with an embodiment of the present invention, for deriving, as a function of weather, the capacity of an airport to handle aircraft departures and aircraft arrivals.

DETAILED DESCRIPTION OF SPECIFIC EMBODIMENTS

FIG. 1 is a block diagram providing an overview of data flow in accordance with a preferred embodiment of a method in accordance with the present invention. In this embodiment, it can be seen from the diagram that estimating the amount of delay in flight departure in accordance with the box 14 depends on three distinct processes. In accordance with process 11, the departure airport conditions delay contribution is determined based on departure airport conditions. Similarly, in accordance with process 12, the arrival airport conditions delay contribution is determined based on arrival airport conditions. Arrival airport conditions delay is computed in a manner analogous to departure airport conditions delay, but often has less importance as a contributor to delay in a departing flight when the planned flight is over a longer distance, since the arrival airport will have some intervening time to handle problems associated with any diminished capacity; this effect is taken into account by the algorithm described below in connection with FIG. 3. In accordance with process 13, the fleet conditions delay contribution is determined based on fleet conditions. Fleet conditions include determination of when an aircraft is first likely to be available; alternatively or in addition, they include historic flight performance of the fleet. All three of these delay contributions are used in estimating the amount of delay in flight departure in accordance with box 14. In typical embodiments, the amount of delay in departure may be determined by summing all of the delay contributions that are non-overlapping.

In practice, many of the delay contributions are in fact overlapping, and in a simple embodiment, the delay in departure may be determined in box 14 on the basis of the delay contribution that is the greatest. For example, it may be determined in box 13 that fleet conditions will contribute a delay of one hour, and in box 14 that the fleet conditions delay contribution is the greatest of the three delay contributions calculated in boxes 11 through 13. Perhaps there is a delay contribution in box 12 due to arrival airport conditions; yet this contribution is by definition an estimate of the delay in departure caused by conditions at the arrival airport—for example, caused by inclement weather at such airport—and since this delay in departure is concurrent with the delay calculated in box 13 and it is less than the delay calculated in box 13, then it can be discarded in estimating in box 14 the delay. With an approach such as this the initial estimate in box 14 is the system output in a simple embodiment.

Nevertheless, it is still conceivable that arrival airport conditions could contribute a further delay under the circumstances of this example. The delay contribution calculated in box 12 attributable to arrival airport conditions is made (for example) without consideration of the delay contribution from fleet conditions determined in box 13. Given that fleet conditions in this example is contributing a delay of one hour, it may be that, with a one hour delay in departure, the conditions in the arrival airport (projected as of one hour after scheduled arrival time) will be even more congested, causing a hold on the departure of the flight in question. Hence in a further embodiment, after determining an initial amount of delay in box 14, the data from this calculation are used in redetermining the departure airport conditions delay contribution (in box 15), the arrival airport conditions delay contribution (in box 16), and the fleet conditions delay contribution (in box 17). These various delay contributions as redetermined are used to estimate the amount of delay in flight departure in box 18. While we have shown two iterations in the process of delay estimation, it is within the scope of the invention to use further iterations as well, so that the result of the process in box 18 may be used as a further basis for redetermining the respective delay contributions and the resultant overall delay.

For purposes of example, and in a common implementation, the “aircraft” may be an airplane, a “planned” flight may be a scheduled flight of an airplane operated by an airline, and the “fleet” may be the airplanes operated by an airline. We will sometimes therefore refer to an “airplane” and a “scheduled flight” in this context. Additionally, reference to “airline conditions” is in the context of the airline's fleet. Embodiments of the invention, of course, are not limited to this implementation, and is equally applicable to other types and fleets of aircraft and to flights that may be planned but not formally or regularly scheduled.

In implementing embodiments of the present invention, it is not necessary to derive data concerning every airport. For example, some number of airports—of the order of 50 or 100—in the United States accounts for a significant amount of United States air traffic—probably more than 90%—of such air traffic. Accordingly processing data for these 50 to 100 airports provides a reasonably thorough picture of air traffic delay in the United States. Nevertheless, there is considerable benefit in taking into account data for other than these airports, particularly in connection with the evaluation of arrival delays associated with flights to or from such other airports.

The embodiments described herein are implemented using a digital computer system, in some cases advantageously one or more computers coupled to a computer network. The processes described in connection with the figures herein are therefore carried out by use of a digital computer system. In one embodiment, the delay determinations herein are carried out by a computer system pursuant to inquiries provided to the system by users who access the system's resources over a network, including over a network such as the Internet.

For example, the embodiments herein are suitable for use in services analogous to those offered on web sites such as www.usatoday.com and www.travelocity.com. These sites, the content and interface for which are hereby incorporated herein by reference, provide a user an opportunity to use a web browser interface to provide to a web server input data defining a particular airline flight number serving as a query; the server then provides to the user in response to the query a resulting web page containing estimated arrival information for the designated flight. Unless the context otherwise requires, a “user”, for purposes of the present description and the following claims, may include, for example, a potential passenger on an aircraft, an organization or individual whose schedule may be affected by the departure time of an aircraft, and even another computer process, such as a process used for resource management running on the computer system of a relevant organization.

Embodiments herein can provide estimated departure information for a designated flight. In this connection, FIG. 7 is a block diagram of a system, in accordance with an embodiment of the present invention, providing a user interface for accessing departure delay information. In this embodiment, a user entry process 71 permits a user to generate a delay query that provides flight parameters sufficient to determine the aircraft flight in question. The delay query is issued by user over network 70. A delay determination process 72 is in communication over network 70 with the user entry process 71. The delay determination process estimates a delay parameter associated with any delay in departure of the aircraft flight specified by the query; typically the delay determination process may be an embodiment of the methods described in connection with FIGS. 1-5, and may be carried out for example by a system of the type described in connection with FIG. 6. In various embodiments, the delay determination process estimates a delay parameter substantially contemporaneously with the delay query. By “substantially contemporaneously”, we mean in this description and in the claims, either that the query triggers the delay determination process or that the delay determination process is performed for a given flight repetitively over relativey short intervals, for example, every 15 minutes or every 10 minutes or every 5 minutes or even more frequently, so that the results are fresh for the user.

A presentation process 73 is also in communication over the network with the delay determination process 72, and the presentation process 73 presents to the user the delay parameter.

The delay parameter may be any of a variety of different types of information and may be presented in any of a wide range of human-readable and machine-readable formats. In text or speech interface, or graphic format, for example, the delay parameter may be presented as estimated minutes after the scheduled time of departure. Alternatively, the departure information may be presented as an estimate of the most probable time of departure. Alternatively it may be presented as an estimate of the most probable amount of delay in departure. Or it may be presented as an estimate of both of these items. This delay parameter may be presented alternatively (or in addition) as a notification delivered to the user a specified duration before the most probable time of departure. For example, the user may be furnished with a mechanism to be notified (by pager or cell phone or other communications medium provided with a ringer or other alarm arrangement) when it is two hours before the most probable time of departure, so that the user is alerted to leave to go to the departure airport.

Alternatively or in addition, the delay parameter may be a measure of the probability of a delay in departure. The probability may be discrete or continuous. If it is discrete, it may, for example, be bi-valued (delay is probable or not probable) or tri-valued (delay is not probable, somewhat probable, or highly probable). The probability may be presented graphically or in text format or both; or alternatively or in addition using a speech interface. The graphic presentation may for example be a bar graph or a colored icon, with the color indicating the probability of delay. Or, for example, there may be presented the scheduled and estimated departure time, and the estimated time may be presented in a color indicative of the likelihood of delay. In another related and simple format, the departure information may be presented as one of three colors: green, yellow, or red, meaning, respectively, no likely delay in departure, some likely delay in departure, and very likely delay in departure. Of course the delay query herein may, but need not necessarily, utilize web pages over the Internet. Any suitable input interface may be used, including interfaces available from the hosts of the www.phone.com web site (which is hereby incorporated herein by reference) for wireless phones and other devices, so that a mobile user may obtain departure delay information. In addition, in another embodiment, in standard wire-based telephone systems, there may be employed interfaces such as touch-tone, voice recognition, and natural language systems. Indeed, these are not all possible interfaces. It is within the scope of the present invention to employ mixed media, so that, for example, a spoken natural language request made using a wireless phone might trigger a response that is delivered in a graphic format. As a consequence of the implementation of the processes described herein, various embodiments of the invention include a computer system in which is running the processes described herein.

FIG. 2 is a block diagram showing further detail of data flow for calculation of the departure airport conditions delay contribution in accordance with block 11 of FIG. 1. Here it can be seen that the process 23 of determining the departure airport conditions delay contribution depends on determining departure demand in accordance with process 21 and determining departure capacity in accordance with process 22.

FIG. 3 is a block diagram showing further detail of data flow for calculation of the arrival airport conditions delay contribution in accordance with block 12 of FIG. 1. Here it can be seen that the process 33 of determining the arrival airport conditions delay contribution depends on determining arrival demand in accordance with process 31 and determining arrival capacity in accordance with process 32.

FIG. 4 is a block diagram showing yet further detail of the data flow of FIGS. 2 and 3 in connection with the calculation of departure airport conditions delay contribution and arrival airport conditions delay contribution. In this diagram it can be seen that both the determination of departure demand in box 41 and the determination of arrival demand in box 42 depend on accessing a flight schedule database in box 411. Information from the flight schedule database 411 is updated by access to live air traffic data in box 49. Live air traffic data may be obtained directly from air traffic control or air management data, such as data from the FAA, indirectly from devices that monitor air traffic control or air traffic management devices (as by passive radar monitoring), or by monitoring or polling other devices on aircraft (such as transponders or radio transmitters that give, for example, gps-derived position data).

FIG. 4 also shows that determination of departure capacity at the departure airport in accordance with box 43 depends on data from the departure capacity data base 415; the departure capacity data from the data base 415 in turn depend on departure airport weather conditions provided by box 413 and live air traffic data from box 49. In an analogous matter, the determination of arrival capacity at the arrival airport in accordance with box 44 depends on data from the arrival capacity data base 416; the arrival capacity data from the data base 416 in turn depend on the arrival airport weather conditions provided by box 414 and live air traffic data from box 49.

In this connection, weather conditions at a given airport are suitably quantized into, for example, any of 10 different values. Departure capacity and arrival capacity are suitably described in terms of a number of flights per unit of time (takeoffs in connection with departure capacity, landings in connection with arrival capacity) as a function of quantized weather. The unit of time, for example, may be the quarter hour. The database provides historic capacity as a function of each weather value. In each case (for both departure capacity and for arrival capacity determinations), the weather conditions database is preferably updated in real time, so as to correct for long-term extrinsic factors such as runway construction, etc. that affect capacity.

It can also be seen in FIG. 4 that calculation of the departure airport conditions delay involves first determining a preliminary delay contribution in box 451. (This preliminary delay contribution is attributable to impact of weather on departure airport capacity in relation to departure airport demand.) Next there is determined recent average departure delay, and, on the basis of this determination, there is evaluated a second departure delay function in box 45. Similarly, calculation of the arrival airport conditions delay involves first determining a preliminary delay contribution in box 462. Next there is determined recent average arrival delay, and on the basis of this determination there is evaluated a second arrival delay function in box 46.

In connection with FIG. 4, we now discuss in outline form algorithms suitable for the processes just discussed. Preliminarily, it assumed here that the flight has not departed. Available data from the U.S. Federal Aviation Administration (FAA), for example, will indicate whether a flight has departed, and it is within the scope of embodiments of the present invention to determine whether a flight has departed. (It is known in the art to estimate the time of arrival of a departed flight on the basis of available FAA data.) For determining the departure airport conditions preliminary delay contribution in accordance with box 451, a suitable algorithm (the “first embodiment for departure delay”) is as follows:

From the Schedule Database, get SDepAp [the scheduled departure airport] and the SDepTime [scheduled departure time].

If PresentTime>SDepTime and flight has not departed, then ADepTime [assumed departure time of flight]=PresentTime

Else ADepTime=SDepTime.

Departure_Capacity_Calculation:

Get DepApSchedDemand [number of flights scheduled to depart from SDepAp in the specified interval T, initially in this embodiment, 30 minutes before ADepTime] for ADepTime from the Schedule Database. Modify DepApSchedDemand to eliminate those flights which don't actually fly, and add unscheduled flights (such as general aviation, for example) which do fly.—If departure within the hour use live air traffic data for this quantity.

Calculate the CurrentWxCond [quantized weather value—at the ADepTime for SDepAp—here, a value 1-10, affected by factors such as winds, visibility, RVR, precipitation, Obscurations, sky conditions (ceiling, etc.), temp and dewpoint, pressure. Alternatively, a multi-dimensional quantity reflecting quantized weather values of weather variables Z_(k) determined in the manner set forth below in discussion of construction of airport capacity databases]

Retrieve the CurDepCapacityforWx [departure capacity, in number of flights in the specified time interval T, for ADepTime] from the Departure/Arrival Capacity Database for SDepAp and the CurrentWxCond.

If CurDepCapacityforWx<K1Ap [an airport-dependent constant], then increment specified time interval T, and go to Departure_Capacity_Calculation [above, to recalculate based on the larger time interval T].

Else determine DemandDiff=(DepApSchedDemand−CurDepCapacityforWx)

If (DemandDiff<K4ap [an airport-dependent constant]) then WxDelay=0

Else WxDelay=DemandDiff* (T/CurDepCapacityforWx)

Alternatively, WxDelay may be derived from a lookup value from a database calculated from historic data as to the time interval required to restore undelayed service for various values of DemandDiff.

A suitable algorithm for the process of box 45 is as follows:

If time of departure is not within (for example) 5 hours, AvgDepDelay [defined in next paragraph]=0 [When departure time is too remote, do not use current airport conditions to project delay in such flight. Can use weighting based on remoteness of departure time.]

Compute the AvgDepDelay [the average delay of flights departing within a recent time period, such as a half hour, where the delay is the difference between actual and scheduled departure times; see Table 4].

Compute DepDelayTrend [trend in departure delays; extrapolated from data in Table 4], and use to update AvgDepDelay for ADepTime.

If (AvgDepDelay>WxDelay) PrelimDepDelay [preliminary delay contribution for departure airport conditions]=AvgDepDelay

Else PrelimDepDelay=WxDelay

PrelimDepDelay=PrelimDepDelay *F, where F is a function of SDepTime and ADepTime and the departure airport. A suitable function is: ${F = \frac{K5Ap}{1 + \frac{{ADepTime} - {SDepTime}}{T}}},{{where}\quad {K5Ap}\quad {is}\quad {an}\quad {airport}\text{-}{dependant}}$

constant having a typical value of about unity (within an order of magnitude). This function has the effect of decreasing calculated delay in the interval T for aircraft that are increasingly past their scheduled departure times.

The FAA has made available airport delay data, and such data may be used in a variety of ways. (We call, for the purposes of this description and the following claims, airport delay data from an official source, such as the FAA, or other administrative or public or quasi-public organ, “official airport delay data”. In one embodiment, the official airport delay data may be used in lieu of calculating PrelimDepDelay in the manner above. Alternatively, the data may be used to provide a check upon or an update to PrelimDepDelay.

The first embodiment for departure delay, described above, determines the departure airport conditions preliminary delay contribution on the basis of parameters explored systematically beginning only 30 minutes (or whatever computing interval is specified) prior to the scheduled departure time. A more sophisticated approach takes into account systematically parameters running from a period before the query giving rise to the delay calculation all the way, not only to the scheduled departure time, but even beyond this time to a point when the aircraft would be expected to depart. With this approach one sums the difference between departure demand and departure capacity over the entire period, taking into account variations attributable to weather. In addition, the weather-induced delay calculation can be corrected by airport-specific factor, determined from actual recent average departure delay data. This more sophisticated approach (the “second embodiment for departure delay”) can be described as follows:

SDepAp Preliminary Delay (PrelimDepDelay) Calculation

From the Schedule Database, get SDepAp [the scheduled departure airport], the SDepTime [scheduled departure time], SArrTime [scheduled arrival time, for purposes of PrelimArrDelay calculation below] and SArrAp [scheduled arrival airport, also for purposes of PrelimArrDelay calculation below].

Determine delay contributed by a previous flight segment; using procedure analogous to what is described below and add turn-around time; if previous flight segment has landed or is airborne, use real-time values; repeat for delay contributed by all previous flight segments

ADepTime=SDepTime+aggregate delays from previous segments

Define Interval=30 minutes (for example)

Define StartOfInterval(eventtime) as function returning starting time of that time interval (measured in Interval units) during which eventtime occurs. Hence if an eventtime is at 11:12 and Interval=30 minutes, then Startofinterval (11:12) is 11:00.

← Start and stop of interval of ADepTime→

DT0=StartOfInterval(ADepTime)

DT1=StartOfinterval(ADepTime)+Interval

← Start and stop of interval of RequestTime→

RT0=StartOfInterval(RequestTime)

RT1=StartOfInterval(RequestTime)+Interval

Define GetDemand from time0 to time1=the number of flights scheduled to depart from SDepAp from time0 to time1, minus those flights that don't actually fly, plus unscheduled flights that do fly. If time1 has occurred or will occur (for example) within 1 hour after current time, then optionally use live air traffic data for determining the number of flights planned for takeoff.

If RT1 is later than ADepTime { Overflow = GetDemand from (RT1 minus, for example, 2 hours, to RT1) minus those flights whose SDepTime is later than ADepTime T0 = RT1 T1 = RT1 + Interval Go to “Find Out When Flight Can Depart Calculation” }

Demand0=GetDemand from (RequestTime minus, for example, 2 hours) to RequestTime

← Get number of flights that haven't taken off for remainder of interval→

Demand1=GetDemand from RequestTime to RT1

← Get rate of take off→

Get WxCond0 at RT0 for SDepAp [WxCond0 is a quantized weather value—at RT0 for SDepAp—here, a value 1-10, affected by factors such as winds, visibility, RVR, precipitation, Obscurations, sky conditions (ceiling, etc.), temp and dewpoint, pressure. Alternatively, a multi-dimensional quantity reflecting quantized weather values of weather variables Z_(k) determined in the manner set forth below in discussion of construction of airport capacity databases. Calculated in a manner similar to WxCond0 at RT0 is WxCond at T0 for SDepAp; similarly GetDepCapacity[WxCond] is the departure capacity associated with WxCond for the Interval.]

Capacity=GetDepCapacity[ WxCond0 ] at SDepAp

If (ApDepCapacity≠“Not Usable”) and (ApDepCapacity<Capacity), then Capacity=ApDepCapacity[See Table 6 for description of calculation of ApDepCapacity]

TakeoffRate=Capacity/Interval

← How many will not have taken off from now to end of interval→

Overflow=Demand0+Demand1−((RT1−RequestTime) * Takeoff Rate)

If Overflow<0, Overflow=0

← cycle through each interval before ADepTime→

T0=RT1

T1=RT1+Interval

while T0 < DT0 { Demand = GetDemand from T0 through T1 Get WxCond at T0 for SDepAp Capacity = GetDepCapacity [WxCond] at SDepAp If (ApDepCapacity ≠ “Not Usable”) and (ApDepCapacity < Capacity) and (T0 < (RT0 plus (Interval * K11ap [an airport-dependent constant, for example, 4]))), then Capacity = ApDepCapacity TakeoffRate = Capacity/Interval Overflow = Overflow + Demand − ((T1−T0) * TakeoffRate) If Overflow < 0, Overflow = 0 T0 = T1 T1 = T0 + Interval }

← get demand of interval up to ADepTime→

T0=DT0

T1=ADepTime

Demand=GetDemand from T0 to T1

Get WxCond at T0 for SDepAp

Capacity=GetDepCapacity[ WxCond] at SDepAp

If (ApDepCapacity≠“Not Usable”) and (ApDepCapacity<Capacity) and (T0<(RT0 plus (Interval * K11ap [an airport-dependent constant, for example, 4]))),

then Capacity=ApDepCapacity

Takeoff Rate=Capacity/Interval

Overflow=Overflow+Demand−((T1−T0) * TakeoffRate)

If Overflow≦0 then PrelimDepDelay=ADepTime−SDepTime

Else { T0 = ADepTime T1 = DT1 ← Find Out When Flight Can Depart Calculation → PrelimDepDelay = ADepTime − SDepTime Get WxCond at T0 for SDepAp Capacity = GetDepCapacity [WxCond] at SDepAp If (ApDepCapacity ≠ “Not Usable”) and (ApDepCapacity < Capacity) and (T0 < (RT0 plus (Interval * K11ap [an airport-dependent constant, for example, 4]))), then Capacity = ApDepCapacity TakeoffRate = Capacity/Interval If (Overflow − ((T1−T0) * TakeoffRate)) ≦ 0 then PrelimDepDelay = PrelimDepDelay + (Overflow*(Interval/ Capacity)) else { Overflow = Overflow − ((T1−T0) * TakeoffRate) T0 = T1 ← Perform the following until overflow is less than or equal to zero or the PrelimDepDelay is greater than 6 hours → while Overflow > 0 and PrelimDepDelay < KmaxdDep, [an airport- dependent variable, for example, 6hrs] { Get WxCond at T0 for SDepAp Capacity = GetDepCapacity [WxCond] at SDepAp If (ApDepCapacity ≠ “Not Usable”) and (ApDepCapacity < Capacity) and (T0 < (RT0 plus (Interval * K11ap [an airport- dependent constant, for example, 4]))), then Capacity = ApDepCapacity if (Overflow − Capacity) ≦ 0 then PrelimDepDelay = PrelimDepDelay + (Overflow * (Interval/Capacity)) else PrelimDepDelay = PrelimDepDelay + Interval Overflow = Overflow − Capacity T0 = T0 + Interval } }

Analogous algorithms apply to implementing the processes with respect to the arrival airport. For determining the arrival airport conditions preliminary delay contribution in accordance with box 462, a suitable algorithm (the “first embodiment for arrival delay”) is as follows:

From the Schedule Database, get SArrAp [the scheduled arrival airport] and the SArrTime [scheduled arrival time].

If PrelimDepDelay<Kf [a constant, for example, 0.1, which is indicative of the ability of the flight to regain a fraction of the time lost by departure delay] *(SArrTime−SDepTime) then AArrTime [assumed arrival time]=SArrTime

Else AArrTime=SArrTime+PrelimDepDelay

Arrival_Capacity_Calculation:

Get ArrApSchedDemand [number of flights scheduled to arrive—at the AArrTime—at SArrAp in the specified interval T, in this embodiment, initially 30 minutes] for SArrAp from the Schedule Database. Modify ArrApSchedDemand to eliminate those flights which do not actually fly and add unscheduled flights (such as general aviation, for example) which do fly.—If arrival within the hour use live air traffic data for this quantity.

Calculate the CurrentWxCond [quantized weather value—at the AArrTime for SArrAp—for the current weather, here, a value 1-10. Alternatively, a multi-dimensional quantity reflecting quantized weather values of weather variables Z_(k) determined in the manner set forth below in discussion of construction of airport capacity databases].

Retrieve the CurArrCapacityforWx [current arrival capacity in number of flights in the specified time interval T for AArrTime] from the Departure/Arrival Capacity Database for SArrAp and the CurrentWxCond.

If CurArrCapacityforWx<K2Ap [an airport-dependent constant], then increment specified time interval T, and go to Arrival_Capacity_Calculation [above, to recalculate based on the larger time interval T].

Else determine DemandDiff=(ArrApSchedDemand−CurArrCapacityforWx)

If (DemandDiff<K7ap [an airport-dependent constant]) then WxDelay=0

Else WxDelay=DemandDiff* (T/CurArrCapacityforWx)

Alternatively, WxDelay may be derived from a lookup value from a database calculated from historic data as to the time interval required to restore undelayed service for various values of DemandDiff.

A suitable algorithm for the process of box 46 is as follows:

If time of arrival is not within (for example) 5 hours, AvgArrDelay [defined in next paragraph]=0 [When arrival time is too remote, do not use current airport conditions to project delay in such flight. Can use weighting based on remoteness of arrival time.]

Else compute the AvgArrDelay [the average delay of flights arriving within a recent time period, such as a half hour, where the delay is the difference between actual and scheduled arrival times; see Table 5].

Compute ArrDelayTrend [trend in arrival delays; extrapolated from data in Table 5], and use to update AvgArrDelay for AArrTime.

If (AvgArrDelay>WxDelay), then PrelimArrDelay [preliminary delay contribution for arrival airport conditions]=AvgArrDelay

Else PrelimArrDelay=WxDelay

As described previously, the FAA has made available airport delay data, and such official airport delay data may be used in a variety of ways. In a further embodiment, the data may be used in lieu of calculating PrelimArrDelay in the manner above. Alternatively, the data may be used to provide a check upon or an update to PrelimArrDelay.

Just as a more sophisticated embodiment has been described above in connection with departure delay calculations, similarly, there is a more sophisticated embodiment in connection with arrival delay calculations. Accordingly, a more sophisticated embodiment (the “second embodiment for arrival delay”) is as follows:

SArrAp Preliminary Delay (PrelimArrDelay) Calculation

From the SDepAp Preliminary Delay Calculation above, retrieve values for SArrAp [the scheduled arrival airport], SArrTime [scheduled arrival time], SDepTime [scheduled departure time] and PrelimDepDelay.

← add in the Departure Delay unless the flight can make it up enroute→

if PrelimDepDelay<(Kf [for example, 0.1]* (SArrTime−SDepTime)) AArrTime=SArrTime

else AArrTime=SArrTime+PrelimDepDelay, or alternatively the flight's revised arrival time which may include FAA control time, use of available FAA departure slot control times and data.

Define Interval=30 minutes (for example)

Define StartOfinterval(eventtime) as function returning starting time of that time interval (measured in Interval units) during which eventtime occurs. Hence if an eventtime is at 11:12 and Interval=30 minutes, then StartofInterval (11:12) is 11:00.

← Start and stop of interval of RequestTime→

RT0=StartOfinterval(RequestTime)

RT1=StartOfinterval(RequestTime)+Interval

Define GetDemand from time0 to time1=the number of flights scheduled to arrive at SArrAp from time0 to time1, minus those flights that don't actually fly, plus unscheduled flights that do fly. Optionally use live air traffic data, when available, to update schedule information and other derived demand values, and recalculate demand based on updated values.

← Start and stop of interval of AArrTime→

AT0=StartOfInterval(AArrTime)

AT1=StartOfinterval(AArrTime)+Interval

If RT1 is later than AArrTime { Overflow = GetDemand from (RT1 minus, for example, 2 hours, to RT1) minus those flights whose SArrTime is later than AArrTime T0 = RT1 T1 = RT1 + Interval Go To “Find Out When the Flight Can Land Calculation” }

← Number of flights that still have to land at the arrival airport at RequestTime→

Demand0=GetDemand from (RequestTime minus, for example, 2 hours) to RequestTime

Demand1=GetDemand from RequestTime to RT1

← Get rate of landing→

Get WxCond0 at RT0 for SArrAp [WxCond0 is a quantized weather value—at RT0 for SArrApp—here, a value 1-10, affected by factors such as winds, visibility, RVR, precipitation, Obscurations, sky conditions (ceiling, etc.), temp and dewpoint, pressure. Alternatively, a multi-dimensional quantity reflecting quantized weather values of weather variables Z_(k) determined in the manner set forth below in discussion of construction of airport capacity databases. Calculated in a manner similar to WxCond0 at RT0 is WxCond at T0 for SArrAp; similarly GetArrCapacity[WxCond] is the landing capacity associated with WxCond for the Interval.]

Capacity=GetArrCapacity[ WxCond0] at SArrAp

If (ApArrCapacity≠“Not Usable”) and (ApArrCapacity<Capacity) then Capacity=ApArrCapacity [Calculation of ApArrCapacity is wholly analogous to calculation of ApDepCapacity described below in Table 6.]

LandingRate=Capacity/Interval

← How many will not have landed from now to end of interval→

Overflow=Demand0+Demand1−((RT1−RequestTime)*LandingRate)

If Overflow<0, Overflow=0

← cycle through each interval before AArrTime→

T0=RT1

T1=RT1+Interval

while T0 < AT0 { Demand = GetDemand from T0 through T1 Get WxCond at T0 for SArrAp Capacity = GetArrCapacity [WxCond] at SArrAp If (ApArrCapacity ≠ “Not Usable”) and (ApArrCapacity < Capacity) and (T0 < (RT0 plus (Interval * K12ap [an airport-dependent constant, for example, 4]))), then Capacity = ApArrCapacity LandingRate = Capacity/Interval Overflow = Overflow + Demand − ((T1 − T0) * LandingRate) If Overflow < 0, Overflow = 0 T0 = T1 T1 = T0 + Interval }

← get demand of interval up to AArrTime→

T0=AT0

T1=AArrTime

Demand=GetDemand from T0 to T1

Get WxCond at T0 for SArrAp

Capacity=GetArrCapacity[ WxCond ] at SArrAp

If (ApArrCapacity≠“Not Usable”) and (ApArrCapacity<Capacity) and (T0<RT0 plus (Interval* K12ap [an airport-dependent constant, for example, 4])),

then Capacity=ApArrCapacity

LandingRate=Capacity/Interval

Overflow=Overflow+Demand−((T1−T0) * LandingRate)

If Overflow≦0

PrelimArrDelay=AArrTime−SArrTime

Else { T0 = AArrTime T1 = AT1 ← Find Out When The Flight Can Land Calculation → PrelimArrDelay = AArrTime − SArrTime Get WxCond at T0 for SArrAp Capacity = GetArrCapacity [WxCond] at SArrAp If (ApArrCapacity ≠ “Not Usable”) and (ApArrCapacity < Capacity) and (T0 < (RT0 plus (Interval K12ap [an airport-dependent constant, for example, 4]))), then Capacity = ApArrCapacity LandingRate Capacity/Interval If (Overflow − ((T1−T0) * LandingRate)) ≦ 0 then PrelimArrDelay = PrelimArrDelay + (Overflow*(Interval/Capacity)) else { Overflow = Overflow − (LandingRate *(T1−T0)) T0 = T1 ← Perform the following until the overflow is zero or less or the PrelimArrDelay is greater than 6 hours → while overflow > 0 and PrelimArrDelay < KmaxdArr [an airport- dependent variable, for example, 6hrs] { Get WxCond at T0 for SArrAp Capacity = GetArrCapacity [WxCond] at SArrAp If (ApArrCapacity ≠ “Not Usable”) and (ApArrCapacity < Capacity) and (T0 < ((RT0 plus (Interval K12ap [an airport-dependent constant, for example, 4]))), then Capacity = ApArrCapacity If (Overflow − Capacity) < 0 then PrelimArrDelay = PrelimArrDelay + (Overflow * (Interval/Capacity)) else PrelimArrDelay = PrelimArrDelay + Interval Overflow = Overflow − Capacity T0 = T0 + Interval }}

The departure capacity database and the arrival capacity database may optionally be structured on the basis of historic data for each airport to be listed in the database. Broadly, in accordance with an embodiment of our invention illustrated in FIG. 8, we derive, as a function of weather, the capacity of an airport to handle aircraft departures and aircraft arrivals. Initially, as shown in process 81, there is provided the historic flight data for the airport, including, for each of a number specified time intervals (and typically there are many of them), actual departures and actual arrivals, weather conditions, and demand for departures and demand for arrivals. (We consider the historic data to include departure demand and arrival demand if these things can be determined from other available data, such as flight schedule data.)

Next, in process 82, we select occasions in such intervals when demand for departures and demand for arrivals exceed actual arrivals and actual departures. (These occasions are presumptively ones wherein weather has prevented the satisfying of demand; any interval where non-weather incidents are determined to have decreased departures or arrivals are not selected.) In process 83, we quantize weather conditions for such occasions to produce quantized data identifying weather conditions for each such occasion. Finally, in process 84, we determine capacity of the airport to handle aircraft departures and aircraft arrivals from data pertinent to such occasions as a function of quantized weather values. Typically we assume that there is a total capacity, for each quantized weather value, that is the sum of arrival capacity and departure capacity. In this context, normally one expects that priority is granted in providing arrival capacity to service arrival demand ahead of providing departure capacity is to service departure demand.

For each airport, for example, there may first be recorded historic capacity data in relation to weather. Historic capacity data in turn may be inferred from historic departure and arrival data for an airport, by examining the number of departures and arrivals at peak demand times that are likely to tax the capacity of the airport. Peak demand times may be identified by recourse to the calculation of departure demand and arrival demand in the manner described above in connection with blocks 41 and 42 of FIG. 4 and the algorithm described thereafter: the number of flights scheduled minus the number of flights scheduled that typically do not fly plus the number of unscheduled flights that typically fly. Alternatively, or in addition, peak demand times can be identified directly by examination of historic departure and arrival data for periods where the numbers of departures and arrivals are at a maximum (for example, Friday 5:00 pm to 6:00 pm), and excluding instances where one would not expect heavy demand (for example, holidays, such as Christmas and Thanksgiving). The weather may be described contemporaneously in terms of parameters including precipitation rate, precipitation type (for example, rain, snow, hail), visibility, ceiling, wind velocity, wind direction, temperature, time of day, and season. In relation to these parameters are maintained the departure capacity of the airport and the arrival capacity of the airport. Next for departure capacity, for example, the capacity is divided into as many discrete value ranges as there are levels of quantization of weather. For each value range of capacity there are correlated the predominant weather parameter ranges, and an associated quantized weather value. An approach such a just described requires sufficient data as to be statistically significant with respect to capacity under each relevant set of weather conditions.

In a further related embodiment, statistical modeling may be applied to historic data for each airport to be listed in the database to construct weather variables and to estimate quantized weather values. A useful approach is to construct variables such as prevailing visibility, the occurrence of light rain, etc. from weather data; the bivariate relationship between these variables and the number of departures that occurred during the associated peak period (eg, half hour, hour) can be examined for statistical significance. Only peak time intervals are included in this analysis to reduce the amount of non weather-related variation in the number of departures and to assure that what is being predicted is capacity as opposed to simply the number of scheduled departures. Those variables that are found to have a statistically significant relationship with the number of departures are then used as candidate variables in a multivariate statistical model to predict capacity. In some cases, adjacent categories of a variable are combined if these categories do not show by themselves significant predictive discrimination in number of departures.

It is possible that the effects of a candidate variable are no longer statistically significant when its effect is estimated jointly with the other candidate variables in a multivariate model. Only statistically significant variables are included in the final model to increase the likelihood that the model will predict well on additional data obtained in the future.

As an example, the variables, here identified as Z_(k) (k=1, . . . , 6), as constructed may include the following:

Z₁, visibility (<3 miles, ≧3 and <10 miles, ≧10 miles)

Z₂, windspeed (<15 knots, ≧15 knots)

Z₃, snow (none or light snow, heavy snow)

Z₄, rain/thunderstorms (none, light, heavy)

Z₅, drizzle (none, some)

Z₆, weather activity level (0-2 factors present, 3 or more factors present).

These variables may be incorporated in a non-linear regression model to predict capacity using the SPSS GOLDMineR program, available from SPSS, Inc. (see www.spss.com), Chicago, Ill., or using the Latent class regression module in the Latent GOLD program, available from Statistical Innovations Inc. (see www.Latentgold.com), Belmont, Mass. For example, by specifying the number of departures in a peak hour time interval as the “dependent” variable, and applying the “count” option to this variable, values will be estimated for each category of each of the weather variables (specified as nominal predictors in the program) based on a Poisson regression model.

Let Y=actual (historical) number of departures during a given peak hour at a given airport and let Z_(k)(i)=1 if the ith category of the weather variable Z_(k) occurs or is predicted to occur, 0 otherwise. Then, the predicted number of departures (which number is typically a reflection of airport capacity, since we are predicting departures at the given peak hour) at that airport during that hour takes the form

Exp[a+b₁(i)Z₁(i)+b₂(j)Z₂(j)+. . . +b_(K)(m)Z_(K)(m)],

where a, b₁(i), b₂(j), . . . , b_(K)(m) are model parameters that are estimated by the Latent GOLD program to obtain the quantized weather values. i=1,2, . . . ,I; j=1,2, . . . ,J; . . . m=1,2, . . . ,M.

In our example, given the number of categories of these variables, there are a total of 3×2×2×3×2×2 possible combinations of categories, or 144 combinations of weather variables. The model can thus be used to determine airport capacity for each combination of variables, so that a table can be constructed for each airport correlating capacity with each of the 144 combinations.

More generally, as an example, corresponding to the occurrence (or predicted occurrence) of the combination of weather categories Z₁(i),Z₂(j), . . . Z_(K)(m) is the prediction

Exp[a+b₁(i)Z₁(i)+b₂(j)Z₂(j)+. . . +b_(K)(m)Z_(K)(m)]

FIG. 5 is a block diagram showing further detail of the data flow in connection with the fleet conditions delay contribution in accordance with block 13 of FIG. 1. In one embodiment, determination of the fleet conditions delay contribution in box 51 involves first accessing in box 54 a flight segment database. This database correlates, with any given flight, an immediately previous flight segment that is flown by the aircraft to be used in the given flight. (The importance of this database is that if the flight of the immediately previous flight segment is delayed, the aircraft used in the given flight may not be available to depart on schedule.) Information from the flight segment database permits estimating when the immediately previous flight segment aircraft shall have landed at the departure airport in accordance with box 53. Data from this process permit determining in box 52 when an aircraft is first likely to be available. This information then permits determining the fleet conditions delay contribution in box 51.

The flight segment database permits evaluating whether an incoming flight segment will contribute to delay of the planned flight. In practice if the previous flight segment lands at a time within a specified time interval (for example 1 hour) later than the planned departure, there will be a delay contribution equal to the amount by which that time is later than the planned departure plus a turnaround factor. If the previous flight segment lands at a time later than (for example) one hour after the planned departure, then one may consider whether other equipment will be used.

In this connection, there may be employed, as a supplement to the flight segment database, a fleet inventory database, which permits evaluating a fleet owner's current inventory of available equipment at each airport at a given time. “Available” in this context means all aircraft of a type suitable for the planned flight at the airport or due to arrive in a selected interval (for example, 90 minutes) less those aircraft that are assigned to scheduled flights.

Equipment in a fleet may suitably be tracked by tail number, namely the registration number associated with a given aircraft. Indeed, as an alternative to using an airline designator and airline flight number, one may, for example, identify a flight by aircraft tail number, scheduled departure time and departure airport. The flight segment database can optionally be constructed by employing tail numbers to correlate each flight number with a given item of equipment. Gate availability is also a potential factor contributing to departure delay. Gate availability is a function dependent on a number of factors, which may be airport specific and airline specific. Gate availability, as well as take-off and landing slots, may be understood as a factor affecting an airline's capacity at an airport. These factors may be modeled and taken into account in estimating departure delay. In one model they may be treated as a fleet condition. Alternatively, one or more of these factors may be treated in connection with airport capacity (for departures or landings) and handled as an airline-specific limitation on capacity at an airport.

A suitable algorithm for computing the fleet conditions delay contribution is as follows:

Determine arrival time (using predictive methods described above for a flight that has not taken off or using actual or estimated arrival times for a flight that has taken off) at departure airport for immediately previous segment of flight aircraft.

If the arrival time of the immediately previous segment is later than the present flight's scheduled departure time but no later than (for example) 2 hours after the scheduled departure time,

AlDepDelay [airline departure delay]=ArrTimePrev Seg [arrival time of the previous segment]+AirlineTurnFactor [airline turnaround factor, the duration required for the airline to make a landing aircraft ready for takeoff, for example, 20 minutes]−SDepTime [scheduled departure time]

Else [in one embodiment], set AlDepDelay=2 hours, for example.

Else [in another embodiment] check the fleet inventory database for availability of an aircraft not scheduled at departure airport.

If there is an available aircraft then, AlDepDelay=AirlinePrepFactor [airline preparation factor, the duration required for the airline to make a non-scheduled aircraft ready for takeoff, for example, 30 minutes]. If there is not an available aircraft, revert to original AlDepDelay.

As an alternative, or in addition, to the approach described in connection with FIG. 5, the fleet conditions delay contribution may be calculated on the basis of historical performance of the fleet. Such historical performance may be evaluated and tracked on a flight segment basis. Alternatively, or in addition, such historical performance may be evaluated utilizing data from the United States Department of Transportation (or from other sources) as to on-time performance of an airline's fleet; see, for example, in Tables 2 and 3, the field “On time %”, which utilizes such data.

In connection with the processes described above, there are suitably employed a series of databases. The departure capacity database 415 and the arrival capacity database 416 may in fact be implemented by an integrated Departure/Arrival Airport Capacity Database having the structure set forth in Table 1 herein. Additionally there is the Flight Schedule Database 411, having the structure set forth in Table 2 herein. There is also the Flight Segment Database 54, having the structure set forth in Table 3 herein.

A suitable algorithm for creating the Arrival Airport Capacity portion of the Departure/Arrival Airport Capacity Database is as follows:

Each specified interval, in this embodiment, 30 minutes:

For each airport: { Calculate the CurrentWxCond [quantized weather value for the current weather, here, a value 1-10] [See discussion above on calculating the quantized weather value]. Retrieve CurLandingCapacityforWx [initially set to a parameter K16ap, say, 0] for CurrentWxCond from the Airport Arrival Capacity Database. Retrieve ActualArrivals [number of actual arrivals at pertinent airport during a recent time period, such as a half hour], AvgArrDelay [as previously defined, the average delay of flights arriving within the same recent time period], DelayedArrFlightCount [number of landing flights experiencing a delay from scheduled arrival during the same recent time period] from Actual Landings Database. If ActualArrivals > CurLandingCapacityforWx, then CurLandingCapacityforWx = ActualArrivals [update Arrival Airport Capacity Database]. Else calculate new CurLandingCapacityforWx by averaging ActualArrivals with CurLandingCapacityforWx [the Arrival Airport Capacity Database is updated with an average of the previous value of CurLandingCapacityforWx and the ActualArrivals]. One may calculate the average as a trend with a number, for example 25, of previous values of ActualArrivals from peak departure times. }

Similarly, a suitable algorithm for creating the Departure Airport Capacity portion of the Departure/Arrival Airport Capacity Database is as follows:

Each specified interval, in this embodiment, 30 minutes:

For each airport:

Calculate the CurrentWxCond [quantized weather value for the current weather, here, a value 1-10] [See discussion above on calculating the quantized weather value].

Retrieve CurDepCapacityforWx [initially set to K17ap, an airport-dependent variable, say, 0]. for CurrentWxCond from the Airport Departure Capacity Database.

Retrieve ActualDepartures [number of actual departures at pertinent airport during a recent time period, such as a half hour], AvgDepDelay [as previously defined, the average delay of flights departing within the same recent time period], DelayedDepFlightCount [number of departed flights experiencing a delay from scheduled departure during the same recent time period] from Actual Departure Database.

If ActualDepartures>CurDepartureCapacityforWx, then CurDepartureCapacityforWx=ActualDepartures [update Departure Airport Capacity Database].

Else calculate new CurDepartureCapacityforWx by averaging ActualDepartures with CurDepartureCapacityforWx [the Departure Airport Capacity Database is updated with an average of the previous value of CurDepartureCapacityforWx and the ActualDepartures]. One may calculate the average as a trend with a number, for example 25, of previous values of ActualArrivals from peak departure times.

A more sophisticated embodiment may take into account the fact that departure and arrival capacities typically interact with each other. For example, a lower number of arrivals allows an airport to handle a higher number of departures, thus altering its nominal number for departure capacity. A suitable algorithm for creating the Departure Airport Capacity and Arrival Airport Capacity portion of the Departure/Arrival Airport Capacity Database, taking into account this kind of interaction, is set forth beginning in the next paragraph. Certain variables used in this algorithm are derivable from historic data for the airport in question, namely MaxDepForWx[maximum of actual departures under specific weather condition], MaxArrForWx[maximum of actual arrivals under specific weather condition], TotalActualsForWx[maximum of the sum actual departures and actual arrivals under specific weather condition], DepRatioForWx [ratio of departures under specific weather condition to TotalActualsForWx], and ArrRatioForWx [ratio of departures under specific weather condition to TotalActualsForWx]. The historic data from which these quantities are calculated include, for each pertinent time interval, ActualDepartures, ActualArrivals, departure demand (derivable from schedule data), arrival demand (also derivable from schedule data), and CurrentWxCond. Initially MaxDepForWx, MaxArrForWx, TotalActualsForWx are each set to 0 and the algorithm operates on historic data to update these parameters. The alogorithm is as follows:

Each specified interval, in this embodiment, 30 minutes:

For each airport:

Calculate the CurrentWxCond [as described above, quantized weather value for the current weather, here, a value 1-10].

Retrieve MaxDepForWx, MaxArrForWx, and TotalActualsForWx [all initially set to K0ap, an airport-dependent variable, say, 0] for CurrentWxCond from the Airport Departure/Arrival Capacity Database.

Retrieve ActualDepartures and ActualArrivals [number of actual departures and arrivals at pertinent airport during a recent time period relative to the specified interval, such as a half hour] from Actual Landings Database.

DepDemand=GetDemand for departures for the interval

ArrDemand=GetDemand for arrivals for the interval

if (ActualDepartures>MaxDepForWx) then MaxDepForWx=ActualDepartures

if (ActualArrivals>MaxArrForWx) then MaxArrForWx=ActualArrivals

if ((ActualDepartures+ActualArrivals)>TotalActualsforWx, then TotalActualsforWx=ActualDepartures+ActualArrivals

During relevant time periods, for example peak traffic when(DepDemand+ArrDemand)>TotalActualsForWx,determine DepRatioForWx, and ArrRatioForWx.

Use suitable statistical methods in the above determinations to remove outlying data with respect to all of the derived quantities.

Update MaxDepForWx, MaxArrForWx, DepRatioForWx, ArrRatioForWx and TotalActualsForWx in the Airport Departure/Arrival Capacity Database

The new values MaxDepForWx, MaxArrForWx, DepRatioForWx, ArrRatioForWx and TotalActualsForWx may used by the SDepAp Preliminary Delay (PrelimDepDelay) Calculation and the SarrAp Preliminary Delay (PrelimArrDelay) Calculation. For PrelimDepDelay, GetDepCapacity[ WxCond] may perform the following:

Retrieve DepRatioForWx, ArrRatioForWx, MaxDepForWx, MaxArrForWx, and TotalActualsForWx

DepDemand=GetDemand for interval at SDepAp for departures

ArrDemand=GetDemand for interval at SDepAp for arrivals

UnusedSlots=TotalActualsForWx−(the minimum of TotalActualsForWx * ArrRatioForWx, ArrDemand, MaxArrForWx)

DepCapacity=the minimum of(MaxDepForWx, UnusedSlots)

For PrelimArrDelay the GetArrCapacity[ WxCond] would perform the following:

Retrieve DepRatioForWx, ArrRatioForWx, MaxDepForWx, MaxArrForWx, and TotalActualsForWx

DepDemand=GetDemand for interval associated with WxCond at SDepAp for departures

ArrDemand=GetDemand for interval associated with WxCond at SDepAp for arrivals

UnusedSlots=TotalActualsForWx−(the minimum of (TotalActualsForWx * DepRatioForWx), DepDemand, MaxDepForWx)

ArrCapacity=the minimum of(MaxArrForWx, UnusedSlots)

In determining the recent average departure delay in accordance with the process of box 45, it is convenient to build an Actual Departures Table, the structure of which is shown in Table 4 herein. Table 4 also sets forth an algorithm suitable for creating the entries in the table. Similarly, in determining the recent average arrival delay in accordance with the process of box 46, it is convenient to build an Actual Arrivals Table, the structure of which is shown in Table 5 herein. Table 5 also sets forth an algorithm suitable for creating the entries in the table.

FIG. 6 is a block diagram of a system, in accordance with an embodiment of the present invention, for carrying out the methods of FIGS. 1 through 5. A flight departure delay estimation module 64 carries out the process of box 14 in FIG. 1, and optionally the process of box 18 of FIG. 1. The departure airport conditions delay module 61 determines the departure airport conditions delay contribution in the manner of the process of box 11 and optionally of box 15, as well as in the manner of the processes of FIG. 2 and relevant portions of FIG. 4. In this connection, the module 61 is coupled to both the departure capacity database 611 and the departure airport weather conditions input (for determining departure capacity). It is also coupled to the flight schedule database 65 and the live air traffic data 601 (for determining departure demand).

Similarly, the arrival airport conditions delay module 62 determines the arrival airport conditions delay contribution in the manner of the process of box 12 and optionally box 16, as well as in the manner of the processes of FIG. 3 and relevant portions of FIG. 4. In this connection the module 62 is coupled to both the arrival capacity database 621 and the arrival weather conditions input (for determining arrival capacity). It is also coupled to the flight schedule database 65 and the live air traffic data 601 (for determining arrival demand).

The fleet conditions delay module 63 carries out the process of box 13 of FIG. 1, and optionally the process of box 17 of FIG. 1, as well as the processes of FIG. 5. In this connection the module 63 is coupled to the flight segment database 66, the flight schedule database 65, and to live air traffic data 601. The flight segment database 66 and the flight schedule database 65 are updated on the basis of live air traffic data 601 by flight segment database manager 661 and flight schedule database manager 651, which are coupled to the live air traffic data 601. The flight segment database 66 is also in communication with the flight schedule database 65 via an input from the flight segment database manager 661 that is coupled to the flight schedule database manager 651. The output from the flight departure delay estimation module 64 is data pertinent to the delay of a departing flight. The delay data can be calculated on an as-needed basis in response to a query. In another embodiment, however, the delay data can be stored in a suitable storage medium and updated on a recurring basis; and queries as to the delay data can then be directed to appropriate entries in the storage medium.

TABLE 1 Departure/Arrival Airport Capacity Database FieldName Type Content Airport Id Alpha- ICAO code for the airport numeric DCWx1 Numeric Departure capacity for Weather Condition 1 DCWx2 Numeric Departure capacity for Weather Condition 2 DCWx3 Numeric Departure capacity for Weather Condition 3 DCWx4 Numeric Departure capacity for Weather Condition 4 DCWx5 Numeric Departure capacity for Weather Condition 5 DCWx6 Numeric Departure capacity for Weather Condition 6 DCWx7 Numeric Departure capacity for Weather Condition 7 DCWx8 Numeric Departure capacity for Weather Condition 8 DCWx9 Numeric Departure capacity for Weather Condition 9 DCWx10 Numeric Departure capacity for Weather Condition 10 ACWx1 Numeric Arrival capacity for Weather Condition 1 ACWx2 Numeric Arrival capacity for Weather Condition 2 ACWx3 Numeric Arrival capacity for Weather Condition 3 ACWx4 Numeric Arrival capacity for Weather Condition 4 ACWx5 Numeric Arrival capacity for Weather Condition 5 ACWx6 Numeric Arrival capacity for Weather Condition 6 ACWx7 Numeric Arrival capacity for Weather Condition 7 ACWx8 Numeric Arrival capacity for Weather Condition 8 ACWx9 Numeric Arrival capacity for Weather Condition 9 ACWx10 Numeric Arrival capacity for Weather Condition 10

TABLE 2 Flight Schedule Database FieldName Type Content Operating Airline Alphanumeric Code for operating airline Operating Flight # Numeric Flight number for operating airline Booking Airline Alphanumeric Code for booking airline Booking Flight # Numeric Flight number for booking airline Sched Departure Alphanumeric code for departure airport Airport Sched Time of Date/Time Scheduled departure time Departure Sched Arrival Alphanumeric code for arrival airport Airport Sched Time of Date/Time Scheduled arrival time Arrival Day of Week Flown Boolean True when flight flies on day of week Aircraft Type Alphanumeric Type of aircraft flown for this flight Date Last Flown Date Date this flight flew last Code Share Only Boolean True when Flight is not actuall operating flight, only codeshare with another airine On time % Numeric On time % provided to DOT % Actually Flown Numeric % this flight flies as scheduled

TABLE 3 Flight Segment Database FieldName Type Content Booking Airline Alphanumeric Code for booking airline Booking Flight # Numeric Flight number for booking airline Sched Departure Alphanumeric Code for departure airport Airport Sched Arrival Airport Alphanumeric Code for arrival airport Date Last Flown Date Date this flight flew last On time % Numeric On time % provided to DOT % Actually Flown Numeric % this flight flies as scheduled Prev Flight Segment Flight #/DAp/AAp Inbound airline flight for this flight = s aircraft Next Flight Segment Flight #/Dap/AAp Outbound airline flight for this flight = s aircraft Aircraft Type Alphanumeric Aircraft type for this flight

TABLE 4 Actual Departures Table Airport ActualDepartures AvgDepDelay DelayedDepFlightCount To Create Table: For departure interval For each airport Retrieve ActualDepartures, DelayedDepElightCount and AvgDepDelay for airport in Actual Departures Table For each flight departed from airport during departure interval Retrieve SDepTime [scheduled departure time] of flight Increment ActualDepartures DepartureDelay = Departure Time [the flight departure time derived from live air traffic data] − SDepTime If DepartureDelay > K3ap [an airport-dependent constant, for example, 5 minutes] then increment DelayedDepFlightCount Calculate AvgDepDelay as representative of calculated DepartureDelay values for current departure interval. Suitable algorithm is to take average of non-outlier values in current interval. Update ActualDepartures, DelayedDepFlightCount and AvgDepDelay for airport in Actual Departures Table. For trend data, use a FIFO stack of n records with each record similar to that above; when stack is full, oldest record is displaced off the stack; use the contents of the stack to compute trend data.

TABLE 5 Actual Arrivals Table Airport ActualArrivals AvgArrDelay DelayedArrFlightCount To Create Table: For arrival interval For each airport Retrieve ActualArrivals, DelayedArrFlightCount and AvgArrDelay for airport in Actual Arrivals Table For each flight arriving at airport during arrival interval Retrieve SArrTime [scheduled arrival time] of flight Increment ActualArrivals ArrivalDelay = ArrivalTime [the flight arrival time derived from live air traffic data] − SArrTime If ArrivalDelay > K8ap [an airport-dependent constant, such as 5 min.] then increment DelayedArrFlightCount Calculate AvgArrDelay as representative of calculated ArrivalDelay values for current arrival interval. Suitable algorithm is to take average of non-outlier values in current interval. Update ActualArrivals, DelayedArrFlightCount and AvgArrDelay for airport in Actual Arrivals Table. For trend data, use a FIFO stack of n records with each record similar to that above; when stack is full, oldest record is displaced off the stack; use the contents of the stack to compute trend data.

TABLE 6 Derivation of ApDepCapacity Get ApDepCapacity for SDepAp Retrieve ApDepCapacity for SDepAp from ApDepCapacity Table Below we assume a five-layer factor calculation for each of four airports. Similar structure for n airports and m-layer factor calculation, m and n being integers. (See discussion in text for explanation of terms not here defined.) This example addresses a range of temporary non-weather conditions affecting airport capacity; however, in cases where capacity has been found to be inhibited for an extended period of time, it may be appropriate, for example, to recompute capacities associated with the airport, taking into account the diminished capacities, in a manner discussed in text. ApDepCapacity Table Format Ap0, ApDepCapacity, ApDepCapacity0, ApDepCapacity1, ApDepCapacity2, ApDepCapacity3 Ap1, ApDepCapacity, ApDepCapacity0, ApDepCapacity1, ApDepCapacity2, ApDepCapacity3 Ap2, ApDepCapacity, ApDepCapacity0, ApDepCapacity1, ApDepCapacity2, ApDepCapacity3 Ap3, ApDepCapacity, ApDepCapacity0, ApDepCapacity1, ApDepCapacity2, ApDepCapacity3 ApDepCapacity Table is calculated each interval for previous interval where T1 is end of previous interval, for each airport Ap T0 = T1 − Interval DepActuals = Number of flights departed during interval from T0 through T1 DepDemand = GetDemand for departures at Ap from T0 to T1 - see description of GetDemand above. Get WxCond at T0 for Ap Capacity = GetDepCapacity [WxCond] at Ap if DepActuals ≧ Capacity, then ApCapacity = DepActuals else if (Dep Demand > (Capacity * K9ap [an airport-dependent constant, for example, 0.95])) and (DepDemand ≧ (DepActuals * K10ap [an airport-dependent constant, for example, 0.75])), then ApCapacity = DepActuals else ApCapacity = “Not Usable” If [for example] any value (ApCapacity, ApDepCapacity0, ApDepCapacity1, etc.) = “Not Useable”, then AveCapacity = “Not Useable” Else AveCapacity = average of ApCapacity, ApDepCapacity0, ... ApDepCapacity(m−1) ApDepCapacity3 = ApDepCapacity2 ApDepCapacity2 = ApDepCapacity1 ApDepCapacity1 = ApDepCapacity0 ApDepCapacity0 = ApCapacity ApDepCapacity = AveCapacity 

What is claimed is:
 1. A method of deriving, as a function of weather, the capacity of an airport to handle aircraft departures and aircraft arrivals, the method comprising: providing historical flight data for airport, over a plurality of specified time intervals, including actual departures and actual arrivals, weather conditions, and demand for departures and demand for arrivals; selecting occasions in such intervals when demand for departures and demand for such arrivals exceeds actual arrivals and actual departures; quantizing weather conditions for such occasions to produce quantized data identifying weather conditions for each such occasion; and determining capacity of the airport to handle aircraft departures and aircraft arrivals from data pertinent to such occasions as a function of quantized weather values.
 2. A method according to claim 1, wherein determining capacity of the airport includes assuming, for each quantized weather value, a total capacity for departures and arrivals, such total capacity being the sum of arrival capacity and departure capacity.
 3. A method according to claim 2, wherein assuming such total capacity further includes assuming a priority for providing arrival capacity sufficient to service arrival demand ahead of providing departure capacity sufficient to service departure demand.
 4. A method according to claim 1, wherein determining capacity of the airport to handle aircraft departures and aircraft arrivals as a function of quantized weather values is accomplished by utilizing statistical analysis of the historical data. 